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At least 991 records · Page 55

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DOE OSTI2023

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is “the ultimate goal” of computational systems biology and “a grand challenge for 21st century”. These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of “system” models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Unstructured mesh-based multi-physics reactor transient simulation with iMC code

In this study, we present a time-dependent Monte Carlo simulation result about a complex fuel-loaded light water reactor system with thermal-hydraulic feedback using the KAIST iMC code. The intra-pin temperature distribution is calculated based on Monte Carlo-tallied detailed power distribution and the finite element heat transfer method. The temperature effect of fuel, absorber, and coolant are adequately reflected into the dynamic response by the on-the-fly Doppler broadened cross-section correction. For the time-dependent Monte Carlo transport simulation, the predictor-corrector quasi-static Monte Carlo method is used for this study. We tested the coupled analysis framework with a 17-by-17 fuel assembly loaded with the centrally-shielded burnable absorber (CSBA) fuel element. The temperature feedback effect on the material cross-section and the moderator density incurred negative reactivity in response to the external positive reactivity insertion, showing the inherent stability of the reactor system. The pin-wise power and temperature change during the system transient are presented and discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1981

An approach to real-time simulation using parallel processing

Current applications of real-time simulations to the development of complex aircraft propulsion system controls have demonstrated the need for accurate, portable, and low-cost simulators. This paper presents a preliminary simulator design that uses a parallel computer organization to provide these features. The hardware and software for this prototype simulator are discussed. A detailed discussion of the inter-computer data transfer mechanism is also presented.

Blech, R. A.↗

NASA NTRS2015

NASA Controller Acceptability Study 1(CAS-1) Experiment Description and Initial Observations

This paper describes the Controller Acceptability Study 1 (CAS-1) experiment that was conducted by NASA Langley Research Center personnel from January through March 2014 and presents partial CAS-1 results. CAS-1 employed 14 air traffic controller volunteers as research subjects to assess the viability of simulated future unmanned aircraft systems (UAS) operating alongside manned aircraft in moderate-density, moderate-complexity Class E airspace. These simulated UAS were equipped with a prototype pilot-in-the-loop (PITL) Detect and Avoid (DAA) system, specifically the Self-Separation (SS) function of such a system based on Stratway+ software to replace the see-and-avoid capabilities of manned aircraft pilots. A quantitative CAS-1 objective was to determine horizontal miss distance (HMD) values for SS encounters that were most acceptable to air traffic controllers, specifically HMD values that were assessed as neither unsafely small nor disruptively large. HMD values between 0.5 and 3.0 nautical miles (nmi) were assessed for a wide array of encounter geometries between UAS and manned aircraft. The paper includes brief introductory material about DAA systems and their SS functions, followed by descriptions of the CAS-1 simulation environment, prototype PITL SS capability, and experiment design, and concludes with presentation and discussion of partial CAS-1 data and results.

Chamberlain, James P.↗

NASA NTRS2015

Forward Bay Cover Separation Modeling and Testing for the Orion Multi-Purpose Crew Vehicle

Spacecraft multi-body separation events during atmospheric descent require complex testing and analysis to validate the flight separation dynamics models used to verify no re-contact. The NASA Orion Multi-Purpose Crew Vehicle (MPCV) architecture includes a highly-integrated Forward Bay Cover (FBC) jettison assembly design that combines parachutes and piston thrusters to separate the FBC from the Crew Module (CM) and avoid re-contact. A multi-disciplinary team across numerous organizations examined key model parameters and risk areas to develop a robust but affordable test campaign in order to validate and verify the FBC separation event for Exploration Flight Test-1 (EFT-1). The FBC jettison simulation model is highly complex, consisting of dozens of parameters varied simultaneously, with numerous multi-parameter interactions (coupling and feedback) among the various model elements, and encompassing distinct near-field, mid-field, and far-field regimes. The test campaign was composed of component-level testing (for example gas-piston thrusters and parachute mortars), ground FBC jettison tests, and FBC jettison air-drop tests that were accomplished by a highly multi-disciplinary team. Three ground jettison tests isolated the testing of mechanisms and structures to anchor the simulation models excluding aerodynamic effects. Subsequently, two air-drop tests added aerodynamic and parachute elements, and served as integrated system demonstrations, which had been preliminarily explored during the Orion Pad Abort-1 (PA-1) flight test in May 2010. Both ground and drop tests provided extensive data to validate analytical models and to verify the FBC jettison event for EFT-1. Additional testing will be required to support human certification of this separation event, for which NASA and Lockheed Martin are applying knowledge from Apollo and EFT-1 testing and modeling to develop a robust human-rated FBC separation event.

Ali, Yasmin↗

NASA NTRS1992

Monte Carlo simulation of a near-continuum shock-shock interaction problem

A complex shock interaction is calculated with direct simulation Monte Carlo (DSMC). The calculation is performed for the near-continuum flow produced when an incident shock impinges on the bow shock of a 0.1 in. radius cowl lip for freestream conditions of approximately Mach 15 and 35 km altitude. Solutions are presented both for a full finite-rate chemistry calculation and for a case with chemical reactions suppressed. In each case, both the undisturbed flow about the cowl lip and the full shock interaction flowfields are calculated. Good agreement has been obtained between the no-chemistry simulation of the undisturbed flow and a perfect gas solution obtained with the viscous shock-layer method. Large differences in calculated surface properties when different chemical models are used demonstrate the necessity of adequately representing the chemistry when making surface property predictions. Preliminary grid refinement studies make it possible to estimate the accuracy of the solutions.

Carlson, Ann B.↗

DOE OSTI2022

Development of MKIDs for Measurement of the Cosmic Microwave Background with the South Pole Telescope

We present details of the design, simulation, and initial test results of prototype detectors for the fourth-generation receiver of the South Pole Telescope (SPT). Optimized for the detection of key secondary anisotropies of the cosmic microwave background (CMB), SPT-3G+ will measure the temperature and polarization of the mm/sub-mm sky at 220, 285, and 345 GHz, beyond the peak of the CMB blackbody spectrum. The SPT-3G+ focal plane will be populated with microwave kinetic inductance detectors (MKIDs), allowing for significantly increased detector density with reduced cryogenic complexity. We present simulation-backed designs for single-color dual-polarization MKID pixels at each SPT-3G+ observation frequency. We further describe design choices made to promote resonator quality and uniformity, enabling us to maximize the available readout bandwidth. We also discuss aspects of the fabrication process that enable rapid production of these devices and present an initial dark characterization of a series of prototype devices.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Intercomparison of Thermal Regime Algorithms in 1-D Lake Models

Lakes are an important component of the global weather and climate system, but the modeling of their thermal regimes has shown large uncertainties due to the highly diverse lake properties and model configurations. Here we evaluate the algorithms of four key lake thermal processes including turbulent heat fluxes, wind-driven mixing, light extinction, and snow density, using a highly diverse lake dataset provided by the Inter-sectoral Impact Model Intercomparison Project (ISIMIP) 2a lake sector. Algorithm codes are configured and run separately within the same parent model to rule out any interference from factors apart from the algorithms examined. Evaluations are based on both simulation accuracy and recalibration complexity for application to global lakes. For turbulent heat fluxes, the non-Monin-Obukhov similarity (MOS) based, more simplified algorithms perform better in predicting lake epilimnion temperatures and achieve high convergence in the values of the calibrated parameters. For wind-driven mixing, a two-algorithm strategy considering lake shape and season is suggested with the regular mixing algorithm used for spring and earlier summer and the mixing-enhanced algorithm for summer steady stratification and fall overturn periods. There are no evident differences in the simulated thermocline depths using different light extinction algorithms or the observation. Finally, for lake ice phenology, a constant snow density at around 110 kg m-3 is found to be sufficient for most northern lakes while the Arctic lakes require a higher value. Our study provides highly practical guides for improving 1-D lake models and feasible parameterization strategies to better simulate global lake thermal regimes.

58 GEOSCIENCES↗

DOE OSTI2023

Bridging adsorption behavior of confined CH 4 -CO 2 binary mixtures across scales

An accurate understanding of the competitive adsorption of CH 4 -CO 2 binary mixtures in nano-confined systems is critical for engineering CO 2 storage in shale gas reservoirs. Due to difficulties in making reliable experimental observations in nano-scale, atomistic simulations (ASs), such as the Grand Canonical Monte Carlo (GCMC) method, provide a viable approach to studying the adsorption behavior of confined fluids. ASs are, however, limited in the size of the compositional domain due to the high computational cost. This work proposes a framework that combines AS and the lattice Boltzmann (LB) method to bridge the physics of confined fluids across scales. The Peng–Robinson equation of state (PR-EoS) produces fugacity coefficients, which serve as input for conducting multi-component GCMC simulations. These GCMC simulations explore the competitive adsorption behavior of CH 4 -CO 2 in nano-slits at various composition, pressure, and channel-width conditions. Both components generate adsorption layers with high densities near the walls with CO 2 preferentially adsorbing compared to CH 4 on the organic walls of carbon sheets. At the mesoscale, a pseudopotential model represents the intermolecular forces in multi-component, multiple-relaxation-time LB simulations. The LB simulations are in good agreement with the GCMC results, allowing us to obtain values for tunable LB parameters. We then extend the use of LB to simulate adsorption behavior in complex networks with nano-sized channels. The phase behavior and fluid properties in the complex geometries of nano-channels differ from nano-slits and bulk systems. Furthermore, the bridging of physics from GCMC (microscale) to LB (mesoscale) via the macroscale PR-EoS connects the adsorption behavior of binary systems across scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Applying deep learning methods to develop new models of molecular charge transfer, nonadiabatic dynamics, and nonlinear spectroscopy in the condensed phase

Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Inverse design of pore wall chemistry and topology through active learning of surface group interactions

Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. Furthermore, this proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"

This package contains the data and scripts used in "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling" (Jiang et al., 2022). The ARW_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the American River Watershed. The Portage_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the Portage River Watershed. The src folder contains the source code of sensitivity analysis and plotting functions. The notebooks folder contains the Jupyter notebooks for performing sensitivity analysis and other post analyses.Global Sensitivity Analysis (GSA) often is applied to assess the sensitivity of model outputs to their inputs using ensemble simulations. However, increasing model complexity and the associated computational cost have limited the use of most GSA approaches for process-based watershed models. We propose to use Mutual Information (MI) as a computationally efficient GSA method for watershed modeling. Such MI computed from several hundred realizations usually can capture nonlinear relationships between inputs and outputs of interest. We perform MI-based watershed sensitivity analyses in studies of the Portage River Watershed in Ohio and the American River Watershed in Washington. In these studies, MI is used to evaluate the sensitivity of river discharges simulated by the Soil and Water Assessment Tool to no less than 20 parameters for each watershed. Our MI-based sensitivity analyses achieved convergence with about 300~500 realizations, a small fraction of the ensemble size (i.e., several thousand) required by the Sobol method. Nevertheless, MI yields similar sensitivity ranking compared to the Sobol method, especially for sensitive parameters. Our study thus sheds new light on the use of MI as an affordable GSA method for computationally intensive models such as the hyper-resolution, watershed hydrobiogeochemical models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Development of a predictive capability of short-pulse laser-driven broadband x-ray radiography

High intensity, short-pulse laser interaction with a solid metal target produces broadband hard x-rays potentially for various applications of x-ray radiography. In this work, experimental benchmarking of numerical modelling for short-pulse laser-driven broadband x-ray radiography is presented. Angular dependent x-ray spectra are first calculated with a hybrid particle-in-cell code, Large Scale Plasma (LSP), using fast electron parameters inferred from an analysis of measured bremsstrahlung signals. Subsequently, a calculated x-ray spectrum in the direction of radiography is used in photon transport calculations using a Monte Carlo code, Particle and Heavy Ion Transport code System (PHITS), to simulate a radiographic image including a modelled 3D test object, an x-ray attenuation filter and an image plate detector. Simulated radiographic images are compared with measurements obtained in an experiment using a 50-TW Leopard short-pulse laser at the University of Nevada Reno. Results show that simulations reproduce the experimental images well for three different attenuation filters (plastic, aluminium, and brass), while 1D transmission profiles for the plastic and aluminium filters are quantitatively in good agreement. The modelling approach established in this work could be used as a predictive tool to simulate radiographic images of complex 3D solid objects at any arbitrary angular position or to optimize experimental components such as the source spectrum, x-ray attenuation filters and a detector type depending on a radiographic object without carrying out radiographic experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

High-fidelity wind farm simulation methodology with experimental validation

The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University’s National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.

17 WIND ENERGY↗

NASA NTRS2016

Tangle-Free Mesh Motion for Ablation Simulations

Problems involving mesh motion-which should not be mistakenly associated with moving mesh methods, a class of adaptive mesh redistribution techniques-are of critical importance in numerical simulations of the thermal response of melting and ablative materials. Ablation is the process by which material vaporizes or otherwise erodes due to strong heating. Accurate modeling of such materials is of the utmost importance in design of passive thermal protection systems ("heatshields") for spacecraft, the layer of the vehicle that ensures survival of crew and craft during re-entry. In an explicit mesh motion approach, a complete thermal solve is first performed. Afterwards, the thermal response is used to determine surface recession rates. These values are then used to generate boundary conditions for an a posteriori correction designed to update the location of the mesh nodes. Most often, linear elastic or biharmonic equations are used to model this material response, traditionally in a finite element framework so that complex geometries can be simulated. A simple scheme for moving the boundary nodes involves receding along the surface normals. However, for all but the simplest problem geometries, evolution in time following such a scheme will eventually bring the mesh to intersect and "tangle" with itself, inducing failure. This presentation demonstrates a comprehensive and sophisticated scheme that analyzes the local geometry of each node with help from user-provided clues to eliminate the tangle and enable simulations on a wide-class of difficult problem geometries. The method developed is demonstrated for linear elastic equations but is general enough that it may be adapted to other modeling equations. The presentation will explicate the inner workings of the tangle-free mesh motion algorithm for both two and three-dimensional meshes. It will show abstract examples of the method's success, including a verification problem that demonstrates its accuracy and correctness. The focus of the presentation will be on the algorithm; specifics on how the techniques may be used in spacecraft design will be not discussed.

Droba, Justin↗

DOE OSTI2024

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org

Science & Technology - Other Topics↗

NASA NTRS2016

Statistical Emulator for Expensive Classification Simulators

Expensive simulators prevent any kind of meaningful analysis to be performed on the phenomena they model. To get around this problem the concept of using a statistical emulator as a surrogate representation of the simulator was introduced in the 1980's. Presently, simulators have become more and more complex and as a result running a single example on these simulators is very expensive and can take days to weeks or even months. Many new techniques have been introduced, termed criteria, which sequentially select the next best (most informative to the emulator) point that should be run on the simulator. These criteria methods allow for the creation of an emulator with only a small number of simulator runs. We follow and extend this framework to expensive classification simulators.

Ross, Jerret↗

NASA NTRS2013

Victim Simulator for Victim Detection Radar

Testing of victim detection radars has traditionally used human subjects who volunteer to be buried in, or climb into a space within, a rubble pile. This is not only uncomfortable, but can be hazardous or impractical when typical disaster scenarios are considered, including fire, mud, or liquid waste. Human subjects are also inconsistent from day to day (i.e., they do not have the same radar properties), so quantitative performance testing is difficult. Finally, testing a multiple-victim scenario is difficult and expensive because of the need for multiple human subjects who must all be coordinated. The solution is an anthropomorphic dummy with dielectric properties that replicate those of a human, and that has motions comparable to human motions for breathing and heartbeat. Two airfilled bladders filled and drained by solenoid valves provide the underlying motion for vinyl bags filled with a dielectric gel with realistic properties. The entire assembly is contained within a neoprene wetsuit serving as a "skin." The solenoids are controlled by a microcontroller, which can generate a variety of heart and breathing patterns, as well as being reprogrammable for more complex activities. Previous electromagnetic simulators or RF phantoms have been oriented towards assessing RF safety, e.g., the measurement of specific absorption rate (SAR) from a cell phone signal, or to provide a calibration target for diagnostic techniques (e.g., MRI). They are optimized for precise dielectric performance, and are typically rigid and immovable. This device is movable and "positionable," and has motion that replicates the small-scale motion of humans. It is soft (much as human tissue is) and has programmable motions.

Lux, James P.↗